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Automatic Kernel Construction During the Neural Network Learning by Modified Fast Singular Value Decomposition 24th

Automatic Kernel Construction During the Neural Network Lear...

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24th International Conference on Computational science (ICCS)

作者： Jankowski, Norbert Dudek, Grzegorz Nicolaus Copernicus Univ Dept Informat Torun Poland Czestochowa Tech Univ Fac Elect Engn Czestochowa Poland

ISBN: (纸本)9783031637582;9783031637599

Thanks to the broad application fields, learning neural networks is still a more significant problem nowadays. Any attempt in the construction of faster learning algorithms is highly well come. This article presents a new way of learning neural networks with kernels with modified pseudo-inverse learning by modified SVD. The new algorithm constructs the kernels during the learning and estimates the right number in the results. There is no longer a need to define their number of kernels before the learning. This means there is no need to test networks with a number of kernels that is too large, and the number of kernels is no longer a parameter in the selection process (in cross-validation). The results show that the proposed algorithm constructs reasonable kernel bases, and final neural networks are accurate in classification.

关键词： neural network learning kernels classification singular value decomposition

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Twinkle: Threshold Signatures from DDH with Full Adaptive Security 43rd

Twinkle: Threshold Signatures from DDH with Full Adaptive Se...

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43rd Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT)

作者： Bacho, Renas Loss, Julian Tessaro, Stefano Wagner, Benedikt Zhu, Chenzhi CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA Saarland Univ Saarbrucken Germany

ISBN: (纸本)9783031587153;9783031587160

Sparkle is the first threshold signature scheme in the pairing-free discrete logarithm setting (Crites, Komlo, Maller, Crypto 2023) to be proven secure under adaptive corruptions. However, without using the algebraic group model, Sparkle's proof imposes an undesirable restriction on the adversary. Namely, for a signing threshold t < n, the adversary is restricted to corrupt at most t/2 parties. In addition, Sparkle's proof relies on a strong one-more assumption. In this work, we propose Twinkle, a new threshold signature scheme in the pairing-free setting which overcomes these limitations. Twinkle is the first pairing-free scheme to have a security proof under up to t adaptive corruptions without relying on the algebraic group model. It is also the first such scheme with a security proof under adaptive corruptions from a well-studied non-interactive assumption, namely, the Decisional Diffie-Hellman (DDH) assumption. We achieve our result in two steps. First, we design a generic scheme based on a linear function that satisfies several abstract properties and prove its adaptive security under a suitable one-more assumption related to this function. In the context of this proof, we also identify a gap in the security proof of Sparkle and develop new techniques to overcome this issue. Second, we give a suitable instantiation of the function for which the corresponding one-more assumption follows from DDH.

关键词： Threshold Signatures Adaptive Security Pairing-Free Non-Interactive Assumptions

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Testing Compositionality 20th

Testing Compositionality

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20th International Conference on Formal Aspects of Component Software (FACS)

作者： van Cuyck, Gijs van Arragon, Lars Tretmans, Jan Radboud Univ Nijmegen Inst iCIS Nijmegen Netherlands TNO ESI Eindhoven Netherlands

ISBN: (纸本)9783031712609;9783031712616

Compositionality supports the manipulation of large systems by working on their components. For model-based testing, this means that large systems can be tested by modelling and testing their components: passing tests for all components implies passing tests for the whole system. In previous work [14], we defined mutual acceptance for specification models and proved that this is a sufficient condition for compositionality in model-based testing. In this paper, we present an algorithm for verifying mutual acceptance on specifications and a sound and exhaustive model-based test procedure for checking mutual acceptance on black-box implementations, both inspired by the idea of environmental conformance [8,9]. The result is that correctness of large systems can be determined by testing the component implementations for conformance to their component specification and for environmental conformance to the specification of their environment.

关键词： Model checking

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Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression 18th

Simultaneous Model-Based Evolution of Constants and Expressi...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Koch, Johannes Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands Delft Univ Technol Delft Netherlands Leiden Univ Med Ctr Leiden Netherlands

ISBN: (纸本)9783031700545;9783031700552

Genetic programming (GP) approaches are among the state-of-the-art for symbolic regression, the task of constructing symbolic expressions that fit well with data. To find highly accurate symbolic expressions, both the expression structure and any contained real-valued constants, are important. GP-GOMEA, a modern model-based evolutionary algorithm, is one of the leading algorithms for finding accurate, yet compact expressions. Yet, GP-GOMEA does not perform dedicated constant optimization, but rather uses ephemeral random constants. Hence, the accuracy of GP-GOMEA may well still be improved upon by the incorporation of a constant optimization mechanism. Existing research into mixed discrete-continuous optimization with EAs has shown that a simultaneous and well-integrated approach to optimizing both discrete and continuous parts, leads to the best results on a variety of problems, especially when there are interactions between these parts. In this paper, we therefore propose a novel approach where constants in expressions are optimized at the same time as the expression structure by merging the real-valued variant of GOMEA with GP-GOMEA. The proposed approach is compared to other forms of handling constants in GP-GOMEA, and in the context of other commonly used techniques such as linear scaling, restarts, and constant tuning after GP optimization. Our results indicate that our novel approach generally performs best and confirms the importance of simultaneous constant optimization during evolution.

关键词： Genetic programming Constant optimization Symbolic regression Model-based evolutionary algorithms

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A Dynamic Tracking Framework Based on Scene Perception 6th

A Dynamic Tracking Framework Based on Scene Perception

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6th Chinese Conference on Pattern Recognition and computer Vision (PRCV)

作者： Zhang, Jinpu Li, Ziwen Wang, Yuehuan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9789819985548;9789819985555

While recent large models have greatly improved tracking performance, not all scenes require a large and complex network. Dynamic networks can adapt the architecture to different inputs, leading to notable accuracy and computational efficiency. However, existing dynamic architectures and decision mechanisms designed for classification are not applicable to the tracking task. This paper proposes a dynamic tracking framework based on scene perception, named DynamicTrack. We classify tracking scenes into easy and hard categories, and propose a dynamic architecture with an easy-hard dual-branch to handle different scenes respectively. Unlike previous works in classification that selectively prune a subset of the backbone, complete execution of the entire backbone is necessary for tracking. Hence, we maintain two complete transformer backbones for the dual branches and vary the number of input tokens to achieve modeling at different granularities. Then, we propose a scene router that automatically selects the optimal branch for each input frame. The router directly assesses the scene complexity of features extracted by the easy branch for decision-making, without relying on the tracking head output. This enhances decision efficiency during dynamic inference. Moreover, we introduce two techniques that benefit DynamicTrack optimization, namely, the Gumbel-Softmax trick and cross-branch transmission (CBT). The former increases the stochasticity of decisions and prevents mode collapse into trivial solutions. The latter establishes information transmission between the two branches, facilitating discriminative power and learning efficiency. Extensive experiments on four benchmarks demonstrate that the proposed DynamicTrack achieves SOTA performance and accuracy-speed trade-offs.

关键词： Object tracking Dynamic network Scene router

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Evaluating the Effect of Adapting Virtual Humans Based on Individual Differences in Users 15th

Evaluating the Effect of Adapting Virtual Humans Based on In...

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15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Zalake, Mohan De Siqueira, Alexandre Gomes Vaddiparti, Krishna Antonenko, Pavlo Lok, Benjamin Univ Illinois Chicago IL 60607 USA Univ Florida Gainesville FL USA

ISBN: (纸本)9783031610592;9783031610608

This paper investigates the effects of adapting a virtual human's persuasion strategy based on users' personalities and prior beliefs regarding recommended behavior in the context of promoting mental health coping skills among college students. The paper uses the Theory of Planned Behavior (TPB) as the theoretical model to study how a virtual human's persuasion strategies impact behavior change. The paper also employs Cialdini's six persuasion strategies - Reciprocity, Scarcity, Authority, Commitment, Likability, and Consensus - to manipulate the virtual human's dialog. The paper develops a user model that predicts the effectiveness of different persuasion strategies based on user data from a previous study. The paper then evaluates the user model in an empirical study with 292 undergraduate students, comparing three experimental conditions - a matched condition where the virtual human used a more effective persuasion strategy, a mismatched condition where the virtual human used a less effective persuasion strategy, and a control condition where the virtual human did not use any persuasion strategy. The paper finds that adapting the virtual human's persuasion strategy can positively influence users who have low self-efficacy to perform the recommended behavior, but can negatively influence users who already have high self-efficacy. The paper also finds that persuasion strategies may not be sufficient to induce behavior change, and suggests accounting for users' perceived barriers and benefits of the recommended behavior. The paper contributes to the Human-computer Interaction research by providing evidence for the importance of individual differences in designing virtual human health interventions.

关键词： Virtual humans Health Behavior change Adaptive systems

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Flutist: Parallelizing Transaction Processing for LSM-Tree-Based Relational Database 8th

Flutist: Parallelizing Transaction Processing for LSM-Tree-B...

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8th International Joint Conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Xu, Qing Chen, Meng Yang, Yunxin Wang, Chundong ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom A Shanghai Peoples R China

ISBN: (纸本)9789819772377;9789819772384

Log-structured merge tree (LSM-tree) is a promising structure. LSM-tree-based relational databases gain wide popularity in production environments to serve web and big data applications. We take MyRocks for study and explore how to optimize it in handling transactions that concurrent clients issue. Our quantitative study shows that MyRocks severely suffers from the sequential commits of transactions with RocksDB. We accordingly propose an algorithm named Flutist. Flutist employs a holistic strategy to explicitly encode and pass the committing order information across layers of MyRocks. Leveraging such information, Flutist parallelizes transaction processing through concurrent commits with RocksDB and preserves a desired committing order. Experiments confirm that Flutist achieves high performance and portability. Its throughput is up to 5.1x and 5.4x that of MyRocks and state-of-the-art PASV, respectively.

关键词： Relational database systems

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PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips 21st

PyGraft: Configurable Generation of Synthetic Schemas and Kn...

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21st International Conference on The Semantic Web (ESWC)

作者： Hubert, Nicolas Monnin, Pierre d'Aquin, Mathieu Monticolo, Davy Brun, Armelle Univ Lorraine ERPI Nancy France Univ Lorraine CNRS LORIA Nancy France Univ Cote Azur CNRS I3S INRIA Sophia Antipolis France

ISBN: (纸本)9783031606342;9783031606359

Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://***/nicolas-hbt/pygraft.

关键词： Knowledge Graph Schema Semantic Web Synthetic Data Generator

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Improved Golden Sine II in Synergy with Non-monopolized Local Search Strategy 1

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15th International Conference of the Metaheuristics International Conference (MIC)

作者： Valdivia, Arturo Aranguren, Itzel Ramos-Frutos, Jorge Casas-Ordaz, Angel Oliva, Diego Zapotecas-Martinez, Saul Univ Guadalajara Div Tecnol Para Integrac Ciber Humana CUCEI Guadalajara 44430 Jalisco Mexico Ctr Innovac Aplicada Tecnol Competit Posgrados Guanajuato 37545 Mexico Inst Nacl Astrofis Opt & Electr Comp Sci Dept INAOE Puebla 72840 Mexico

ISBN: (数字)9783031629228

ISBN: (纸本)9783031629211;9783031629228

This study introduces an innovative optimization technique rooted in hybridizing the Golden Sine Algorithm II and the Non Monopolized Search algorithm tailored to address unconstrained problems. The core concept underlying Golden Sine Algorithm II hinges on leveraging the diminishing pattern of the sine function and the golden ratio to navigate the solution landscape effectively;meanwhile, the Non-Monopolized Search is employed to improve the exploitation as a local search mechanism. Our proposal is called improved Golden Sine Algorithm II with Non-Monopolized Local Search (GSII-LS). Notably, GSIILS is designed to complement and enhance existing optimization methodologies, working in synergy with non-monopolizing search strategies. To assess its efficacy, GSII-LS is subjected to rigorous testing across 34 benchmark functions for unconstrained optimization. Comparative analysis against optimization algorithms is conducted using established evaluation criteria. Results demonstrate that GSII-LS consistently achieves superior convergence towards global optima across numerous benchmark functions.

关键词： Golden Sine II Non-Monopolized Search Hybridization Metaheuristic algorithms

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Detecting Cells in Histopathology Images with a ResNet Ensemble Model 5th

Detecting Cells in Histopathology Images with a ResNet Ensem...

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5th International Workshop on Graphs in Biomedical Image Analysis (GRAIL) / Workshop on Overlapped Celi on Tissue - Cell Detection from Cell-Tissue Interaction Challenge (OCELOT) / 5th MICCAI Workshop / 1st MICCAI Challenge WorkshopInternational Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Lafarge, Maxime W. Koelzer, Viktor Hendrik Univ Zurich Univ Hosp Zurich Dept Pathol & Mol Pathol Zurich Switzerland

ISBN: (纸本)9783031550874;9783031550881

This paper presents a candidate solution that was submitted to the test phase of the OCELOT2023 challenge. This solution is based on an ensemble of trained fully convolutional ResNet-50 models that were developed using only the small field-of-view (FoV) images with cell-level annotations of the challenge dataset (the large FoV images with tissue-level annotations were not used). The submitted model achieved a F1-score of 0.673 on the evaluation set of the validation phase. The code to run our submitted trained model is available at: https://***/CTPLab/OCELOT2023_P4ResNet50.

关键词： cell detection cell classification computational pathology deep learning ensemble modeling OCELOT2023

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